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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 601960, 11 pages
Research Article

An Endogenous Project Performance Evaluation Approach Based on Random Forests and IN-PROMETHEE II Methods

1School of Management Science and Engineering, Central University of Finance and Economics, Beijing 100081, China
2School of Civil Engineering, Tsinghua University, Beijing 100084, China
3China Economics and Management Academy, Central University of Finance and Economics, Beijing 100081, China
4School of Statistics and Mathematics, Central University of Finance and Economics, Beijing 100081, China

Received 25 May 2014; Accepted 29 July 2014; Published 28 October 2014

Academic Editor: Tofigh Allahviranloo

Copyright © 2014 Na Xie et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In order to identify the best or poorest alternative project by an overall ranking result in the scenario of assessing multiple infrastructure projects, multicriteria decision aid methods need to be incorporated into evaluating project performance. Most previous methods for assessing infrastructure project performance may not be applicable to frequent cases with numerous evaluation criteria but inadequate observation data. This paper proposed an objective performance evaluation approach from annual field-survey data through Random Forests and IN-PROMETHEE II methods together. Random Forests method is employed to predict performance values under selected criteria as the single-valued performance scores. IN-PROMETHEE II method is further developed to quantify the preference index among different projects under each criterion. By calculating a weighted average of single-criterion preference index, the multicriteria preference index can be obtained to determine the ultimate ranking of alternative projects. A comprehensive empirical study reveals that this approach is able to successfully avoid subjective bias. It is helpful in tracing decisive factors of project performance for practical projects in multicriteria cases. The analysis results have proved that the proposed method can be widely used in performance evaluation of complicated infrastructure projects.